def test_shape_gather(self, shape, indices): indices = np.array(indices) inp = Variable("input", dtype=np.float32, shape=shape) graph = Graph(inputs=[inp]) inp_shape = graph.shape(inp) shape_part = graph.gather(inp_shape, indices=indices) graph.outputs = [ graph.add(shape_part, shape_part), graph.gather(inp_shape, indices=[0]), graph.gather(inp_shape, indices=np.array(0)), ] graph.fold_constants() if shape is not None: assert isinstance(graph.outputs[0], Constant) expected_shape = np.array(shape)[indices].astype(np.int64) * 2 assert np.all(graph.outputs[0].values == expected_shape) else: assert isinstance(graph.outputs[0], Variable) assert isinstance(graph.outputs[1], Variable) assert isinstance(graph.outputs[2], Variable)
def test_shape_of_variable_tensor_multiple_shapes(self): graph = Graph() var = Variable("var", dtype=np.float32, shape=(1, 3, 4)) var2 = Variable("var2", dtype=np.float32, shape=tuple()) # Scalar graph.inputs = [var, var2] graph.outputs = [ graph.shape(var), graph.identity(var), graph.shape(var2) ] graph.fold_constants().cleanup() assert len(graph.nodes) == 1 assert graph.nodes[0].op == "Identity" assert isinstance(graph.outputs[0], Constant) assert np.all(graph.outputs[0].values == (1, 3, 4)) assert isinstance(graph.outputs[2], Constant) assert np.all(graph.outputs[2].values == tuple())
def test_shape_of_variable_tensor_dynamic_shape(self): var = Variable("var", dtype=np.float32, shape=("", -1, 0, 4)) graph = Graph(inputs=[var]) graph.outputs = [graph.shape(var)] graph.fold_constants().cleanup() assert len(graph.nodes) == 1 assert graph.nodes[0].op == "Shape" assert isinstance(graph.outputs[0], Variable)
def test_shape_of_variable_tensor_static_shape_no_fold(self): graph = Graph() var = Variable("var", dtype=np.float32, shape=(1, 3, 4)) graph.inputs = [var] graph.outputs = [graph.shape(var)] graph.fold_constants(fold_shapes=False).cleanup() assert len(graph.nodes) == 1 assert graph.nodes[0].op == "Shape" assert isinstance(graph.outputs[0], Variable)
def test_shape_of_variable_tensor_static_shape(self): var = Variable("var", dtype=np.float32, shape=(1, 3, 4)) graph = Graph(inputs=[var]) graph.inputs = [var] graph.outputs = [graph.shape(var)] graph.fold_constants().cleanup() assert not graph.nodes assert isinstance(graph.outputs[0], Constant) assert np.all(graph.outputs[0].values == (1, 3, 4))
def test_shape_of_constant_node(self): graph = Graph() values = np.ones((1, 3, 3), dtype=np.int64) const = graph.constant(values=values) graph.outputs = [graph.shape(const)] graph.fold_constants().cleanup() assert not graph.nodes assert isinstance(graph.outputs[0], Constant) assert np.all(graph.outputs[0].values == (1, 3, 3))
def test_shape_slice_single_input(self): inp = Variable("input", dtype=np.int64, shape=(5, 6, 3, 2)) graph = Graph(inputs=[inp]) inp_shape = graph.shape(inp) graph.outputs = [graph.slice(inp_shape)] slice_node = graph.outputs[0].inputs[0] slice_node.attrs = { "axes": [0], "starts": [1], "ends": [3], "steps": [2], } graph.fold_constants() assert isinstance(graph.outputs[0], Constant) assert np.all(graph.outputs[0].values == inp.shape[1:3:2])
def test_shape_slice(self, shape, starts, ends, axes, steps, expected): inp = Variable("input", dtype=np.float32, shape=shape) graph = Graph(inputs=[inp]) inp_shape = graph.shape(inp) graph.outputs = [ graph.slice(inp_shape, np.array(starts), np.array(ends), axes=np.array(axes), steps=np.array(steps)) ] graph.fold_constants() if expected: assert isinstance(graph.outputs[0], Constant) assert np.all(graph.outputs[0].values == expected) else: assert isinstance(graph.outputs[0], Variable)